Overview

Dataset statistics

Number of variables35
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory402.1 KiB
Average record size in memory280.1 B

Variable types

Numeric15
Boolean3
Categorical17

Alerts

EmployeeCount has constant value "1"Constant
Over18 has constant value "True"Constant
StandardHours has constant value "80"Constant
EmployeeNumber has unique valuesUnique
NumCompaniesWorked has 197 (13.4%) zerosZeros
TrainingTimesLastYear has 54 (3.7%) zerosZeros
YearsAtCompany has 44 (3.0%) zerosZeros
YearsInCurrentRole has 244 (16.6%) zerosZeros
YearsSinceLastPromotion has 581 (39.5%) zerosZeros
YearsWithCurrManager has 263 (17.9%) zerosZeros

Reproduction

Analysis started2024-08-13 05:00:12.604961
Analysis finished2024-08-13 05:00:27.375678
Duration14.77 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:27.806452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2024-08-12T22:00:27.883752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35 78
 
5.3%
34 77
 
5.2%
36 69
 
4.7%
31 69
 
4.7%
29 68
 
4.6%
32 61
 
4.1%
30 60
 
4.1%
33 58
 
3.9%
38 58
 
3.9%
40 57
 
3.9%
Other values (33) 815
55.4%
ValueCountFrequency (%)
18 8
 
0.5%
19 9
 
0.6%
20 11
 
0.7%
21 13
 
0.9%
22 16
 
1.1%
23 14
 
1.0%
24 26
1.8%
25 26
1.8%
26 39
2.7%
27 48
3.3%
ValueCountFrequency (%)
60 5
 
0.3%
59 10
0.7%
58 14
1.0%
57 4
 
0.3%
56 14
1.0%
55 22
1.5%
54 18
1.2%
53 19
1.3%
52 18
1.2%
51 19
1.3%

Attrition
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1233 
True
237 
ValueCountFrequency (%)
False 1233
83.9%
True 237
 
16.1%
2024-08-12T22:00:27.953088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

BusinessTravel
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Travel_Rarely
1043 
Travel_Frequently
277 
Non-Travel
150 

Length

Max length17
Median length13
Mean length13.447619
Min length10

Characters and Unicode

Total characters19768
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTravel_Rarely
2nd rowTravel_Frequently
3rd rowTravel_Rarely
4th rowTravel_Frequently
5th rowTravel_Rarely

Common Values

ValueCountFrequency (%)
Travel_Rarely 1043
71.0%
Travel_Frequently 277
 
18.8%
Non-Travel 150
 
10.2%

Length

2024-08-12T22:00:28.018888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:28.082332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
travel_rarely 1043
71.0%
travel_frequently 277
 
18.8%
non-travel 150
 
10.2%

Most occurring characters

ValueCountFrequency (%)
e 3067
15.5%
r 2790
14.1%
l 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
y 1320
6.7%
_ 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3067
15.5%
r 2790
14.1%
l 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
y 1320
6.7%
_ 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3067
15.5%
r 2790
14.1%
l 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
y 1320
6.7%
_ 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3067
15.5%
r 2790
14.1%
l 2790
14.1%
a 2513
12.7%
T 1470
7.4%
v 1470
7.4%
y 1320
6.7%
_ 1320
6.7%
R 1043
 
5.3%
n 427
 
2.2%
Other values (7) 1558
7.9%

DailyRate
Real number (ℝ)

Distinct886
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.48571
Minimum102
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:28.151324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile165.35
Q1465
median802
Q31157
95-th percentile1424.1
Maximum1499
Range1397
Interquartile range (IQR)692

Descriptive statistics

Standard deviation403.5091
Coefficient of variation (CV)0.50282403
Kurtosis-1.2038228
Mean802.48571
Median Absolute Deviation (MAD)344
Skewness-0.0035185684
Sum1179654
Variance162819.59
MonotonicityNot monotonic
2024-08-12T22:00:28.227654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.4%
408 5
 
0.3%
530 5
 
0.3%
1329 5
 
0.3%
1082 5
 
0.3%
329 5
 
0.3%
829 4
 
0.3%
1469 4
 
0.3%
267 4
 
0.3%
217 4
 
0.3%
Other values (876) 1423
96.8%
ValueCountFrequency (%)
102 1
 
0.1%
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.2%
115 1
 
0.1%
116 2
0.1%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.1%
1495 3
0.2%
1492 1
 
0.1%
1490 4
0.3%
1488 1
 
0.1%
1485 3
0.2%
1482 1
 
0.1%
1480 2
0.1%

Department
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Research & Development
961 
Sales
446 
Human Resources
 
63

Length

Max length22
Median length22
Mean length16.542177
Min length5

Characters and Unicode

Total characters24317
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowResearch & Development
3rd rowResearch & Development
4th rowResearch & Development
5th rowResearch & Development

Common Values

ValueCountFrequency (%)
Research & Development 961
65.4%
Sales 446
30.3%
Human Resources 63
 
4.3%

Length

2024-08-12T22:00:28.310324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:28.372651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
research 961
27.8%
961
27.8%
development 961
27.8%
sales 446
12.9%
human 63
 
1.8%
resources 63
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24317
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24317
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24317
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5377
22.1%
1985
 
8.2%
s 1533
 
6.3%
a 1470
 
6.0%
l 1407
 
5.8%
R 1024
 
4.2%
r 1024
 
4.2%
c 1024
 
4.2%
n 1024
 
4.2%
m 1024
 
4.2%
Other values (10) 7425
30.5%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:28.431726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2024-08-12T22:00:28.496599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 211
14.4%
1 208
14.1%
10 86
 
5.9%
9 85
 
5.8%
3 84
 
5.7%
7 84
 
5.7%
8 80
 
5.4%
5 65
 
4.4%
4 64
 
4.4%
6 59
 
4.0%
Other values (19) 444
30.2%
ValueCountFrequency (%)
1 208
14.1%
2 211
14.4%
3 84
 
5.7%
4 64
 
4.4%
5 65
 
4.4%
6 59
 
4.0%
7 84
 
5.7%
8 80
 
5.4%
9 85
5.8%
10 86
5.9%
ValueCountFrequency (%)
29 27
1.8%
28 23
1.6%
27 12
0.8%
26 25
1.7%
25 25
1.7%
24 28
1.9%
23 27
1.8%
22 19
1.3%
21 18
1.2%
20 25
1.7%

Education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Length

2024-08-12T22:00:28.559099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:28.619098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 572
38.9%
4 398
27.1%
2 282
19.2%
1 170
 
11.6%
5 48
 
3.3%

EducationField
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Life Sciences
606 
Medical
464 
Marketing
159 
Technical Degree
132 
Other
82 

Length

Max length16
Median length15
Mean length10.533333
Min length5

Characters and Unicode

Total characters15484
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLife Sciences
2nd rowLife Sciences
3rd rowOther
4th rowLife Sciences
5th rowMedical

Common Values

ValueCountFrequency (%)
Life Sciences 606
41.2%
Medical 464
31.6%
Marketing 159
 
10.8%
Technical Degree 132
 
9.0%
Other 82
 
5.6%
Human Resources 27
 
1.8%

Length

2024-08-12T22:00:28.699024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:28.773168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
life 606
27.1%
sciences 606
27.1%
medical 464
20.8%
marketing 159
 
7.1%
technical 132
 
5.9%
degree 132
 
5.9%
other 82
 
3.7%
human 27
 
1.2%
resources 27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e 3105
20.1%
i 1967
12.7%
c 1967
12.7%
n 924
 
6.0%
a 782
 
5.1%
765
 
4.9%
s 660
 
4.3%
M 623
 
4.0%
L 606
 
3.9%
f 606
 
3.9%
Other values (16) 3479
22.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15484
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3105
20.1%
i 1967
12.7%
c 1967
12.7%
n 924
 
6.0%
a 782
 
5.1%
765
 
4.9%
s 660
 
4.3%
M 623
 
4.0%
L 606
 
3.9%
f 606
 
3.9%
Other values (16) 3479
22.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15484
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3105
20.1%
i 1967
12.7%
c 1967
12.7%
n 924
 
6.0%
a 782
 
5.1%
765
 
4.9%
s 660
 
4.3%
M 623
 
4.0%
L 606
 
3.9%
f 606
 
3.9%
Other values (16) 3479
22.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15484
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3105
20.1%
i 1967
12.7%
c 1967
12.7%
n 924
 
6.0%
a 782
 
5.1%
765
 
4.9%
s 660
 
4.3%
M 623
 
4.0%
L 606
 
3.9%
f 606
 
3.9%
Other values (16) 3479
22.5%

EmployeeCount
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
1470 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1470
100.0%

Length

2024-08-12T22:00:28.860017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:28.930699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 1470
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1470
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1470
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1470
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1470
100.0%

EmployeeNumber
Real number (ℝ)

UNIQUE 

Distinct1470
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1024.8653
Minimum1
Maximum2068
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:29.008611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile96.45
Q1491.25
median1020.5
Q31555.75
95-th percentile1967.55
Maximum2068
Range2067
Interquartile range (IQR)1064.5

Descriptive statistics

Standard deviation602.02433
Coefficient of variation (CV)0.58741801
Kurtosis-1.2231789
Mean1024.8653
Median Absolute Deviation (MAD)533.5
Skewness0.01657402
Sum1506552
Variance362433.3
MonotonicityStrictly increasing
2024-08-12T22:00:29.087526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1391 1
 
0.1%
1389 1
 
0.1%
1387 1
 
0.1%
1383 1
 
0.1%
1382 1
 
0.1%
1380 1
 
0.1%
1379 1
 
0.1%
1377 1
 
0.1%
1375 1
 
0.1%
Other values (1460) 1460
99.3%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
4 1
0.1%
5 1
0.1%
7 1
0.1%
8 1
0.1%
10 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
ValueCountFrequency (%)
2068 1
0.1%
2065 1
0.1%
2064 1
0.1%
2062 1
0.1%
2061 1
0.1%
2060 1
0.1%
2057 1
0.1%
2056 1
0.1%
2055 1
0.1%
2054 1
0.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Length

2024-08-12T22:00:29.158612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:29.216624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring characters

ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 453
30.8%
4 446
30.3%
2 287
19.5%
1 284
19.3%

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Male
882 
Female
588 

Length

Max length6
Median length4
Mean length4.8
Min length4

Characters and Unicode

Total characters7056
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 882
60.0%
Female 588
40.0%

Length

2024-08-12T22:00:29.283201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:29.340127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
male 882
60.0%
female 588
40.0%

Most occurring characters

ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2058
29.2%
a 1470
20.8%
l 1470
20.8%
M 882
12.5%
F 588
 
8.3%
m 588
 
8.3%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.891156
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:29.412571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q383.75
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)35.75

Descriptive statistics

Standard deviation20.329428
Coefficient of variation (CV)0.30853044
Kurtosis-1.1963985
Mean65.891156
Median Absolute Deviation (MAD)18
Skewness-0.032310953
Sum96860
Variance413.28563
MonotonicityNot monotonic
2024-08-12T22:00:29.512279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 29
 
2.0%
98 28
 
1.9%
42 28
 
1.9%
48 28
 
1.9%
84 28
 
1.9%
57 27
 
1.8%
79 27
 
1.8%
96 27
 
1.8%
54 26
 
1.8%
52 26
 
1.8%
Other values (61) 1196
81.4%
ValueCountFrequency (%)
30 19
1.3%
31 15
1.0%
32 24
1.6%
33 19
1.3%
34 12
0.8%
35 18
1.2%
36 18
1.2%
37 18
1.2%
38 13
0.9%
39 17
1.2%
ValueCountFrequency (%)
100 19
1.3%
99 20
1.4%
98 28
1.9%
97 21
1.4%
96 27
1.8%
95 23
1.6%
94 22
1.5%
93 16
1.1%
92 25
1.7%
91 18
1.2%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Length

2024-08-12T22:00:29.589136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:29.652157image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 868
59.0%
2 375
25.5%
4 144
 
9.8%
1 83
 
5.6%

JobLevel
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Length

2024-08-12T22:00:29.728403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:29.790629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 543
36.9%
2 534
36.3%
3 218
14.8%
4 106
 
7.2%
5 69
 
4.7%

JobRole
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Sales Executive
326 
Research Scientist
292 
Laboratory Technician
259 
Manufacturing Director
145 
Healthcare Representative
131 
Other values (4)
317 

Length

Max length25
Median length21
Mean length18.070748
Min length7

Characters and Unicode

Total characters26564
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales Executive
2nd rowResearch Scientist
3rd rowLaboratory Technician
4th rowResearch Scientist
5th rowLaboratory Technician

Common Values

ValueCountFrequency (%)
Sales Executive 326
22.2%
Research Scientist 292
19.9%
Laboratory Technician 259
17.6%
Manufacturing Director 145
9.9%
Healthcare Representative 131
8.9%
Manager 102
 
6.9%
Sales Representative 83
 
5.6%
Research Director 80
 
5.4%
Human Resources 52
 
3.5%

Length

2024-08-12T22:00:29.860826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:29.930721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
sales 409
14.4%
research 372
13.1%
executive 326
11.5%
scientist 292
10.3%
laboratory 259
9.1%
technician 259
9.1%
director 225
7.9%
representative 214
7.5%
manufacturing 145
 
5.1%
healthcare 131
 
4.6%
Other values (3) 206
7.3%

Most occurring characters

ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3905
14.7%
a 2580
 
9.7%
t 2098
 
7.9%
c 2061
 
7.8%
i 2012
 
7.6%
r 1984
 
7.5%
n 1468
 
5.5%
s 1391
 
5.2%
1368
 
5.1%
o 795
 
3.0%
Other values (19) 6902
26.0%

JobSatisfaction
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Length

2024-08-12T22:00:30.013794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:30.067633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring characters

ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 459
31.2%
3 442
30.1%
1 289
19.7%
2 280
19.0%

MaritalStatus
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
Married
673 
Single
470 
Divorced
327 

Length

Max length8
Median length7
Mean length6.9027211
Min length6

Characters and Unicode

Total characters10147
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowMarried

Common Values

ValueCountFrequency (%)
Married 673
45.8%
Single 470
32.0%
Divorced 327
22.2%

Length

2024-08-12T22:00:30.136670image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:30.197628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
married 673
45.8%
single 470
32.0%
divorced 327
22.2%

Most occurring characters

ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
M 673
6.6%
a 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
M 673
6.6%
a 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
M 673
6.6%
a 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1673
16.5%
i 1470
14.5%
e 1470
14.5%
d 1000
9.9%
M 673
6.6%
a 673
6.6%
S 470
 
4.6%
n 470
 
4.6%
g 470
 
4.6%
l 470
 
4.6%
Other values (4) 1308
12.9%

MonthlyIncome
Real number (ℝ)

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:30.261001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2024-08-12T22:00:30.334784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
6142 3
 
0.2%
2741 3
 
0.2%
2559 3
 
0.2%
2610 3
 
0.2%
2451 3
 
0.2%
5562 3
 
0.2%
3452 3
 
0.2%
2380 3
 
0.2%
6347 3
 
0.2%
Other values (1339) 1439
97.9%
ValueCountFrequency (%)
1009 1
0.1%
1051 1
0.1%
1052 1
0.1%
1081 1
0.1%
1091 1
0.1%
1102 1
0.1%
1118 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19943 1
0.1%
19926 1
0.1%
19859 1
0.1%
19847 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%

MonthlyRate
Real number (ℝ)

Distinct1427
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14313.103
Minimum2094
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:30.406635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2094
5-th percentile3384.55
Q18047
median14235.5
Q320461.5
95-th percentile25431.9
Maximum26999
Range24905
Interquartile range (IQR)12414.5

Descriptive statistics

Standard deviation7117.786
Coefficient of variation (CV)0.4972916
Kurtosis-1.2149561
Mean14313.103
Median Absolute Deviation (MAD)6206.5
Skewness0.018577808
Sum21040262
Variance50662878
MonotonicityNot monotonic
2024-08-12T22:00:30.476721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4223 3
 
0.2%
9150 3
 
0.2%
9558 2
 
0.1%
12858 2
 
0.1%
22074 2
 
0.1%
25326 2
 
0.1%
9096 2
 
0.1%
13008 2
 
0.1%
12355 2
 
0.1%
7744 2
 
0.1%
Other values (1417) 1448
98.5%
ValueCountFrequency (%)
2094 1
0.1%
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.1%
2137 1
0.1%
2227 1
0.1%
2243 1
0.1%
2253 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26959 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26894 1
0.1%
26862 1
0.1%

NumCompaniesWorked
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:30.534744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2024-08-12T22:00:30.592166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 521
35.4%
0 197
 
13.4%
3 159
 
10.8%
2 146
 
9.9%
4 139
 
9.5%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
9 52
 
3.5%
8 49
 
3.3%
ValueCountFrequency (%)
0 197
 
13.4%
1 521
35.4%
2 146
 
9.9%
3 159
 
10.8%
4 139
 
9.5%
5 63
 
4.3%
6 70
 
4.8%
7 74
 
5.0%
8 49
 
3.3%
9 52
 
3.5%
ValueCountFrequency (%)
9 52
 
3.5%
8 49
 
3.3%
7 74
 
5.0%
6 70
 
4.8%
5 63
 
4.3%
4 139
 
9.5%
3 159
 
10.8%
2 146
 
9.9%
1 521
35.4%
0 197
 
13.4%

Over18
Boolean

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1470 
ValueCountFrequency (%)
True 1470
100.0%
2024-08-12T22:00:30.642139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

OverTime
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
1054 
True
416 
ValueCountFrequency (%)
False 1054
71.7%
True 416
 
28.3%
2024-08-12T22:00:30.685642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

PercentSalaryHike
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:30.742202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2024-08-12T22:00:30.800657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 210
14.3%
13 209
14.2%
14 201
13.7%
12 198
13.5%
15 101
6.9%
18 89
6.1%
17 82
 
5.6%
16 78
 
5.3%
19 76
 
5.2%
22 56
 
3.8%
Other values (5) 170
11.6%
ValueCountFrequency (%)
11 210
14.3%
12 198
13.5%
13 209
14.2%
14 201
13.7%
15 101
6.9%
16 78
 
5.3%
17 82
 
5.6%
18 89
6.1%
19 76
 
5.2%
20 55
 
3.7%
ValueCountFrequency (%)
25 18
 
1.2%
24 21
 
1.4%
23 28
 
1.9%
22 56
3.8%
21 48
3.3%
20 55
3.7%
19 76
5.2%
18 89
6.1%
17 82
5.6%
16 78
5.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Length

2024-08-12T22:00:30.892487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:30.964319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1244
84.6%
4 226
 
15.4%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Length

2024-08-12T22:00:31.027650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:31.089111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring characters

ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 459
31.2%
4 432
29.4%
2 303
20.6%
1 276
18.8%

StandardHours
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
80
1470 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2940
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80
2nd row80
3rd row80
4th row80
5th row80

Common Values

ValueCountFrequency (%)
80 1470
100.0%

Length

2024-08-12T22:00:31.160671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:31.214847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
80 1470
100.0%

Most occurring characters

ValueCountFrequency (%)
8 1470
50.0%
0 1470
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 1470
50.0%
0 1470
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 1470
50.0%
0 1470
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 1470
50.0%
0 1470
50.0%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Length

2024-08-12T22:00:31.270658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:31.329819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 631
42.9%
1 596
40.5%
2 158
 
10.7%
3 85
 
5.8%

TotalWorkingYears
Real number (ℝ)

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:31.402481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2024-08-12T22:00:31.484594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 202
 
13.7%
6 125
 
8.5%
8 103
 
7.0%
9 96
 
6.5%
5 88
 
6.0%
7 81
 
5.5%
1 81
 
5.5%
4 63
 
4.3%
12 48
 
3.3%
3 42
 
2.9%
Other values (30) 541
36.8%
ValueCountFrequency (%)
0 11
 
0.7%
1 81
5.5%
2 31
 
2.1%
3 42
 
2.9%
4 63
4.3%
5 88
6.0%
6 125
8.5%
7 81
5.5%
8 103
7.0%
9 96
6.5%
ValueCountFrequency (%)
40 2
 
0.1%
38 1
 
0.1%
37 4
0.3%
36 6
0.4%
35 3
 
0.2%
34 5
0.3%
33 7
0.5%
32 9
0.6%
31 9
0.6%
30 7
0.5%

TrainingTimesLastYear
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:31.556966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2024-08-12T22:00:31.614664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
1 71
 
4.8%
6 65
 
4.4%
0 54
 
3.7%
ValueCountFrequency (%)
0 54
 
3.7%
1 71
 
4.8%
2 547
37.2%
3 491
33.4%
4 123
 
8.4%
5 119
 
8.1%
6 65
 
4.4%
ValueCountFrequency (%)
6 65
 
4.4%
5 119
 
8.1%
4 123
 
8.4%
3 491
33.4%
2 547
37.2%
1 71
 
4.8%
0 54
 
3.7%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Length

2024-08-12T22:00:31.680156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T22:00:31.738664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 893
60.7%
2 344
 
23.4%
4 153
 
10.4%
1 80
 
5.4%

YearsAtCompany
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:31.814917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2024-08-12T22:00:31.887676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5 196
13.3%
1 171
11.6%
3 128
8.7%
2 127
8.6%
10 120
8.2%
4 110
 
7.5%
7 90
 
6.1%
9 82
 
5.6%
8 80
 
5.4%
6 76
 
5.2%
Other values (27) 290
19.7%
ValueCountFrequency (%)
0 44
 
3.0%
1 171
11.6%
2 127
8.6%
3 128
8.7%
4 110
7.5%
5 196
13.3%
6 76
 
5.2%
7 90
6.1%
8 80
5.4%
9 82
5.6%
ValueCountFrequency (%)
40 1
 
0.1%
37 1
 
0.1%
36 2
 
0.1%
34 1
 
0.1%
33 5
0.3%
32 3
0.2%
31 3
0.2%
30 1
 
0.1%
29 2
 
0.1%
27 2
 
0.1%

YearsInCurrentRole
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:31.949816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2024-08-12T22:00:32.023743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 372
25.3%
0 244
16.6%
7 222
15.1%
3 135
 
9.2%
4 104
 
7.1%
8 89
 
6.1%
9 67
 
4.6%
1 57
 
3.9%
6 37
 
2.5%
5 36
 
2.4%
Other values (9) 107
 
7.3%
ValueCountFrequency (%)
0 244
16.6%
1 57
 
3.9%
2 372
25.3%
3 135
 
9.2%
4 104
 
7.1%
5 36
 
2.4%
6 37
 
2.5%
7 222
15.1%
8 89
 
6.1%
9 67
 
4.6%
ValueCountFrequency (%)
18 2
 
0.1%
17 4
 
0.3%
16 7
 
0.5%
15 8
 
0.5%
14 11
 
0.7%
13 14
 
1.0%
12 10
 
0.7%
11 22
 
1.5%
10 29
2.0%
9 67
4.6%

YearsSinceLastPromotion
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:32.089167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2024-08-12T22:00:32.153002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
7 76
 
5.2%
4 61
 
4.1%
3 52
 
3.5%
5 45
 
3.1%
6 32
 
2.2%
11 24
 
1.6%
8 18
 
1.2%
Other values (6) 65
 
4.4%
ValueCountFrequency (%)
0 581
39.5%
1 357
24.3%
2 159
 
10.8%
3 52
 
3.5%
4 61
 
4.1%
5 45
 
3.1%
6 32
 
2.2%
7 76
 
5.2%
8 18
 
1.2%
9 17
 
1.2%
ValueCountFrequency (%)
15 13
 
0.9%
14 9
 
0.6%
13 10
 
0.7%
12 10
 
0.7%
11 24
 
1.6%
10 6
 
0.4%
9 17
 
1.2%
8 18
 
1.2%
7 76
5.2%
6 32
2.2%

YearsWithCurrManager
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2024-08-12T22:00:32.214599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2024-08-12T22:00:32.276798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 344
23.4%
0 263
17.9%
7 216
14.7%
3 142
9.7%
8 107
 
7.3%
4 98
 
6.7%
1 76
 
5.2%
9 64
 
4.4%
5 31
 
2.1%
6 29
 
2.0%
Other values (8) 100
 
6.8%
ValueCountFrequency (%)
0 263
17.9%
1 76
 
5.2%
2 344
23.4%
3 142
9.7%
4 98
 
6.7%
5 31
 
2.1%
6 29
 
2.0%
7 216
14.7%
8 107
 
7.3%
9 64
 
4.4%
ValueCountFrequency (%)
17 7
 
0.5%
16 2
 
0.1%
15 5
 
0.3%
14 5
 
0.3%
13 14
 
1.0%
12 18
 
1.2%
11 22
 
1.5%
10 27
 
1.8%
9 64
4.4%
8 107
7.3%

Interactions

2024-08-12T22:00:26.049748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:13.059179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-08-12T22:00:18.641059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:19.475673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-08-12T22:00:21.320817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:22.213077image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:23.086874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-08-12T22:00:20.109167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:21.125431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:22.028591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:22.902913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:24.072152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:25.015653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:25.880162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:26.876568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:13.735762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:14.665946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:15.568736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:16.541682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:17.432126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:18.509997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:19.364265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:20.165882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:21.186639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:22.088235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:22.965576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:24.154052image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:25.076666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:25.937759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:26.935572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:13.788447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:14.729870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:15.629900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:16.602639image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:17.492783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:18.578935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:19.417393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:20.218381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:21.253592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:22.151571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:23.025612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:24.212881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:25.132988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-08-12T22:00:25.991954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-08-12T22:00:27.042700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T22:00:27.268579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
041YesTravel_Rarely1102Sales12Life Sciences112Female9432Sales Executive4Single5993194798YYes11318008016405
149NoTravel_Frequently279Research & Development81Life Sciences123Male6122Research Scientist2Married5130249071YNo2344801103310717
237YesTravel_Rarely1373Research & Development22Other144Male9221Laboratory Technician3Single209023966YYes15328007330000
333NoTravel_Frequently1392Research & Development34Life Sciences154Female5631Research Scientist3Married2909231591YYes11338008338730
427NoTravel_Rarely591Research & Development21Medical171Male4031Laboratory Technician2Married3468166329YNo12348016332222
532NoTravel_Frequently1005Research & Development22Life Sciences184Male7931Laboratory Technician4Single3068118640YNo13338008227736
659NoTravel_Rarely1324Research & Development33Medical1103Female8141Laboratory Technician1Married267099644YYes204180312321000
730NoTravel_Rarely1358Research & Development241Life Sciences1114Male6731Laboratory Technician3Divorced2693133351YNo22428011231000
838NoTravel_Frequently216Research & Development233Life Sciences1124Male4423Manufacturing Director3Single952687870YNo214280010239718
936NoTravel_Rarely1299Research & Development273Medical1133Male9432Healthcare Representative3Married5237165776YNo133280217327777
AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumberEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelJobRoleJobSatisfactionMaritalStatusMonthlyIncomeMonthlyRateNumCompaniesWorkedOver18OverTimePercentSalaryHikePerformanceRatingRelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
146029NoTravel_Rarely468Research & Development284Medical120544Female7321Research Scientist1Single378584891YNo14328005315404
146150YesTravel_Rarely410Sales283Marketing120554Male3923Sales Executive1Divorced10854165864YYes133280120333220
146239NoTravel_Rarely722Sales241Marketing120562Female6024Sales Executive4Married1203188280YNo1131801212220996
146331NoNon-Travel325Research & Development53Medical120572Male7432Manufacturing Director1Single993637870YNo193280010239417
146426NoTravel_Rarely1167Sales53Other120604Female3021Sales Representative3Single2966213780YNo18348005234200
146536NoTravel_Frequently884Research & Development232Medical120613Male4142Laboratory Technician4Married2571122904YNo173380117335203
146639NoTravel_Rarely613Research & Development61Medical120624Male4223Healthcare Representative1Married9991214574YNo15318019537717
146727NoTravel_Rarely155Research & Development43Life Sciences120642Male8742Manufacturing Director2Married614251741YYes20428016036203
146849NoTravel_Frequently1023Sales23Medical120654Male6322Sales Executive2Married5390132432YNo143480017329608
146934NoTravel_Rarely628Research & Development83Medical120682Male8242Laboratory Technician3Married4404102282YNo12318006344312